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Econometrics, Volume 6, Issue 2 (June 2018)

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Open AccessArticle Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?
Econometrics 2018, 6(2), 31; https://doi.org/10.3390/econometrics6020031
Received: 17 March 2018 / Revised: 6 June 2018 / Accepted: 11 June 2018 / Published: 15 June 2018
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Abstract
In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of studies document that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality.
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In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of studies document that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality. Contrary to the stationary case, this paper shows for the bivariate VAR(1) system that systematic sampling induces spurious bi-directional Granger causality among the variables if the uni-directional causality runs from a non-stationary series to either a stationary or a non-stationary series. An empirical exercise illustrates the relative usefulness of the results further. Full article
Open AccessArticle Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data
Econometrics 2018, 6(2), 30; https://doi.org/10.3390/econometrics6020030
Received: 1 January 2018 / Revised: 23 May 2018 / Accepted: 23 May 2018 / Published: 4 June 2018
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Abstract
It is sometimes observed and frequently assumed that top incomes in household surveys worldwide are poorly measured and that this problem biases the measurement of income inequality. This paper tests this assumption and compares the performance of reweighting and replacing methods designed to
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It is sometimes observed and frequently assumed that top incomes in household surveys worldwide are poorly measured and that this problem biases the measurement of income inequality. This paper tests this assumption and compares the performance of reweighting and replacing methods designed to correct inequality measures for top-income biases generated by data issues such as unit or item non-response. Results for the European Union’s Statistics on Income and Living Conditions survey indicate that survey response probabilities are negatively associated with income and bias the measurement of inequality downward. Correcting for this bias with reweighting, the Gini coefficient for Europe is revised upwards by 3.7 percentage points. Similar results are reached with replacing of top incomes using values from the Pareto distribution when the cut point for the analysis is below the 95th percentile. For higher cut points, results with replacing are inconsistent suggesting that popular parametric distributions do not mimic real data well at the very top of the income distribution. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
Open AccessArticle The Wall’s Impact in the Occupied West Bank: A Bayesian Approach to Poverty Dynamics Using Repeated Cross-Sections
Econometrics 2018, 6(2), 29; https://doi.org/10.3390/econometrics6020029
Received: 3 December 2017 / Revised: 7 May 2018 / Accepted: 22 May 2018 / Published: 30 May 2018
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Abstract
In 2002, the Israeli government decided to build a wall inside the occupied West Bank. The wall had a marked effect on the access to land and water resources as well as to the Israeli labour market. It is difficult to include the
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In 2002, the Israeli government decided to build a wall inside the occupied West Bank. The wall had a marked effect on the access to land and water resources as well as to the Israeli labour market. It is difficult to include the effect of the wall in an econometric model explaining poverty dynamics as the wall was built in the richer region of the West Bank. So a diff-in-diff strategy is needed. Using a Bayesian approach, we treat our two-period repeated cross-section data set as an incomplete data problem, explaining the income-to-needs ratio as a function of time invariant exogenous variables. This allows us to provide inference results on poverty dynamics. We then build a conditional regression model including a wall variable and state dependence to see how the wall modified the initial results on poverty dynamics. We find that the wall has increased the probability of poverty persistence by 58 percentage points and the probability of poverty entry by 18 percentage points. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessArticle Decomposing Wage Distributions Using Recentered Influence Function Regressions
Econometrics 2018, 6(2), 28; https://doi.org/10.3390/econometrics6020028
Received: 31 December 2017 / Revised: 27 April 2018 / Accepted: 9 May 2018 / Published: 25 May 2018
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Abstract
This paper provides a detailed exposition of an extension of the Oaxaca-Blinder decomposition method that can be applied to various distributional measures. The two-stage procedure first divides distributional changes into a wage structure effect and a composition effect using a reweighting method. Second,
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This paper provides a detailed exposition of an extension of the Oaxaca-Blinder decomposition method that can be applied to various distributional measures. The two-stage procedure first divides distributional changes into a wage structure effect and a composition effect using a reweighting method. Second, the two components are further divided into the contribution of each explanatory variable using recentered influence function (RIF) regressions. We illustrate the practical aspects of the procedure by analyzing how the polarization of U.S. male wages between the late 1980s and the mid 2010s was affected by factors such as de-unionization, education, occupations, and industry changes. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessArticle Structural Break Tests Robust to Regression Misspecification
Econometrics 2018, 6(2), 27; https://doi.org/10.3390/econometrics6020027
Received: 23 December 2017 / Revised: 5 May 2018 / Accepted: 14 May 2018 / Published: 22 May 2018
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Abstract
Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are
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Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart in correctly specified models. Hence, we propose using it as an alternative and complementary test for breaks. We apply the unconditional and conditional mean and variance tests to three US series: unemployment, industrial production growth and interest rates. Both the unconditional and the conditional mean tests detect a break in the mean of interest rates. However, for the other two series, the unconditional mean test does not detect a break, while the conditional mean tests based on dynamic regression models occasionally detect a break, with the implied break-point estimator varying across different dynamic specifications. For all series, the unconditional variance does not detect a break while most tests for the conditional variance do detect a break which also varies across specifications. Full article
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Open AccessArticle Johansen’s Reduced Rank Estimator Is GMM
Econometrics 2018, 6(2), 26; https://doi.org/10.3390/econometrics6020026
Received: 30 January 2018 / Revised: 9 March 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Abstract
The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator under normality developed by Johansen (1988). This includes
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The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator under normality developed by Johansen (1988). This includes the vector error correction model (VECM) of Engle and Granger. It is also shown that GMM tests for reduced rank (cointegration) are algebraically similar to the Gaussian likelihood ratio tests. This shows that normality is not necessary to motivate these estimators and tests. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
Open AccessEditorial Recent Developments in Macro-Econometric Modeling: Theory and Applications
Econometrics 2018, 6(2), 25; https://doi.org/10.3390/econometrics6020025
Received: 6 February 2018 / Revised: 28 April 2018 / Accepted: 2 May 2018 / Published: 14 May 2018
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Abstract
Developments in macro-econometrics have been evolving since the aftermath of the Second World War.[...] Full article
Open AccessArticle A Hybrid MCMC Sampler for Unconditional Quantile Based on Influence Function
Econometrics 2018, 6(2), 24; https://doi.org/10.3390/econometrics6020024
Received: 29 December 2017 / Revised: 25 April 2018 / Accepted: 26 April 2018 / Published: 4 May 2018
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Abstract
In this study, we provide a Bayesian estimation method for the unconditional quantile regression model based on the Re-centered Influence Function (RIF). The method makes use of the dichotomous structure of the RIF and estimates a non-linear probability model by a logistic regression
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In this study, we provide a Bayesian estimation method for the unconditional quantile regression model based on the Re-centered Influence Function (RIF). The method makes use of the dichotomous structure of the RIF and estimates a non-linear probability model by a logistic regression using a Gibbs within a Metropolis-Hastings sampler. This approach performs better in the presence of heavy-tailed distributions. Applied to a nationally-representative household survey, the Senegal Poverty Monitoring Report (2005), the results show that the change in the rate of returns to education across quantiles is substantially lower at the primary level. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessArticle Forecasting Inflation Uncertainty in the G7 Countries
Econometrics 2018, 6(2), 23; https://doi.org/10.3390/econometrics6020023
Received: 16 February 2018 / Revised: 22 February 2018 / Accepted: 16 April 2018 / Published: 27 April 2018
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Abstract
There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA(p,d,q) - MSM(
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There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty. Full article
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Open AccessArticle Parametric Inference for Index Functionals
Econometrics 2018, 6(2), 22; https://doi.org/10.3390/econometrics6020022
Received: 13 December 2017 / Revised: 25 March 2018 / Accepted: 13 April 2018 / Published: 20 April 2018
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Abstract
In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment
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In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment estimator that targets the quantity of interest, namely the considered inequality index. Its primary advantage is that the scale parameter does not need to be estimated to perform parametric bootstrap, since inequality measures are scale invariant. The very good finite sample coverages that are found in a simulation study suggest that this feature provides an advantage over the parametric bootstrap using the maximum likelihood estimator. We also find that overall, a parametric bootstrap provides more accurate inference than its non or semi-parametric counterparts, especially for heavy tailed income distributions. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessReview Using the GB2 Income Distribution
Econometrics 2018, 6(2), 21; https://doi.org/10.3390/econometrics6020021
Received: 9 February 2018 / Revised: 29 March 2018 / Accepted: 4 April 2018 / Published: 18 April 2018
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Abstract
To use the generalized beta distribution of the second kind (GB2) for the analysis of income and other positively skewed distributions, knowledge of estimation methods and the ability to compute quantities of interest from the estimated parameters are required. We review estimation methodology
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To use the generalized beta distribution of the second kind (GB2) for the analysis of income and other positively skewed distributions, knowledge of estimation methods and the ability to compute quantities of interest from the estimated parameters are required. We review estimation methodology that has appeared in the literature, and summarize expressions for inequality, poverty, and pro-poor growth that can be used to compute these measures from GB2 parameter estimates. An application to data from China and Indonesia is provided. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessArticle Polarization and Rising Wage Inequality: Comparing the U.S. and Germany
Econometrics 2018, 6(2), 20; https://doi.org/10.3390/econometrics6020020
Received: 31 January 2018 / Revised: 12 March 2018 / Accepted: 22 March 2018 / Published: 11 April 2018
Cited by 1 | PDF Full-text (1340 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Since the late 1970s, wage inequality has increased strongly both in the U.S. and Germany but the trends have been different. Wage inequality increased along the entire wage distribution during the 1980s in the U.S. and since the mid 1990s in Germany. There
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Since the late 1970s, wage inequality has increased strongly both in the U.S. and Germany but the trends have been different. Wage inequality increased along the entire wage distribution during the 1980s in the U.S. and since the mid 1990s in Germany. There is evidence for wage polarization in the U.S. in the 1990s, and the increase in wage inequality in Germany was restricted to the top of the distribution before the 1990s. Using an approach developed by MaCurdy and Mroz (1995) to separate age, time, and cohort effects, we find a large role played by cohort effects in Germany, while we find only small cohort effects in the U.S. Employment trends in both countries are consistent with polarization since the 1990s. The evidence is consistent with a technology-driven polarization of the labor market, but this cannot explain the country specific differences. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessArticle TSLS and LIML Estimators in Panels with Unobserved Shocks
Econometrics 2018, 6(2), 19; https://doi.org/10.3390/econometrics6020019
Received: 17 August 2017 / Revised: 13 March 2018 / Accepted: 27 March 2018 / Published: 9 April 2018
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Abstract
The properties of the two stage least squares (TSLS) and limited information maximum likelihood (LIML) estimators in panel data models where the observables are affected by common shocks, modelled through unobservable factors, are studied for the case where the time series dimension is
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The properties of the two stage least squares (TSLS) and limited information maximum likelihood (LIML) estimators in panel data models where the observables are affected by common shocks, modelled through unobservable factors, are studied for the case where the time series dimension is fixed. We show that the key assumption in determining the consistency of the panel TSLS and LIML estimators, as the cross section dimension tends to infinity, is the lack of correlation between the factor loadings in the errors and in the exogenous variables—including the instruments—conditional on the common shocks. If this condition fails, both estimators have degenerate distributions. When the panel TSLS and LIML estimators are consistent, they have covariance-matrix mixed-normal distributions asymptotically. Tests on the coefficients can be constructed in the usual way and have standard distributions under the null hypothesis. Full article
Open AccessArticle Decomposing the Bonferroni Inequality Index by Subgroups: Shapley Value and Balance of Inequality
Econometrics 2018, 6(2), 18; https://doi.org/10.3390/econometrics6020018
Received: 11 December 2017 / Revised: 20 March 2018 / Accepted: 23 March 2018 / Published: 2 April 2018
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Abstract
Additive decomposability is an interesting feature of inequality indices which, however, is not always fulfilled; solutions to overcome such an issue have been given by Deutsch and Silber (2007) and by Di Maio and Landoni (2017). In this paper, we apply these methods,
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Additive decomposability is an interesting feature of inequality indices which, however, is not always fulfilled; solutions to overcome such an issue have been given by Deutsch and Silber (2007) and by Di Maio and Landoni (2017). In this paper, we apply these methods, based on the “Shapley value” and the “balance of inequality” respectively, to the Bonferroni inequality index. We also discuss a comparison with the Gini concentration index and highlight interesting properties of the Bonferroni index. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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Open AccessArticle On the Decomposition of the Esteban and Ray Index by Income Sources
Econometrics 2018, 6(2), 17; https://doi.org/10.3390/econometrics6020017
Received: 18 January 2018 / Revised: 22 February 2018 / Accepted: 11 March 2018 / Published: 26 March 2018
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Abstract
This paper proposes a simple algorithm based on a matrix formulation to compute the Esteban and Ray (ER) polarization index. It then shows how the algorithm introduced leads to quite a simple decomposition of polarization by income sources. Such a breakdown
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This paper proposes a simple algorithm based on a matrix formulation to compute the Esteban and Ray (ER) polarization index. It then shows how the algorithm introduced leads to quite a simple decomposition of polarization by income sources. Such a breakdown was not available hitherto. The decomposition we propose will thus allow one to determine the sign, as well as the magnitude, of the impact of the various income sources on the ER polarization index. A simple empirical illustration based on EU data is provided. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
Open AccessArticle Data-Driven Jump Detection Thresholds for Application in Jump Regressions
Econometrics 2018, 6(2), 16; https://doi.org/10.3390/econometrics6020016
Received: 8 January 2018 / Revised: 24 February 2018 / Accepted: 24 February 2018 / Published: 26 March 2018
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Abstract
This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher
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This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher is most likely to encounter that the usual in-fill asymptotics provide a poor guide for selecting the jump threshold. Because of this we develop a sample-based method. Our method estimates the number of jumps over a grid of thresholds and selects the optimal threshold at what we term the ‘take-off’ point in the estimated number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to zero. In several Monte Carlo studies we evaluate the performance of our method based on its ability to accurately locate jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we evaluate the performance of our method in a jump regression context. Finally, we apply our method in two empirical studies. In one we estimate the number of jumps and report the jump threshold our method selects for three commonly used market indices. In the other empirical application we perform a series of jump regressions using our method to select the jump threshold. Full article
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Open AccessArticle Income Inequality, Cohesiveness and Commonality in the Euro Area: A Semi-Parametric Boundary-Free Analysis
Econometrics 2018, 6(2), 15; https://doi.org/10.3390/econometrics6020015
Received: 13 December 2017 / Revised: 22 February 2018 / Accepted: 8 March 2018 / Published: 21 March 2018
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Abstract
The cohesiveness of constituent nations in a confederation such as the Eurozone depends on their equally shared experiences. In terms of household incomes, commonality of distribution across those constituent nations with that of the Eurozone as an entity in itself is of the
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The cohesiveness of constituent nations in a confederation such as the Eurozone depends on their equally shared experiences. In terms of household incomes, commonality of distribution across those constituent nations with that of the Eurozone as an entity in itself is of the essence. Generally, income classification has proceeded by employing “hard”, somewhat arbitrary and contentious boundaries. Here, in an analysis of Eurozone household income distributions over the period 2006–2015, mixture distribution techniques are used to determine the number and size of groups or classes endogenously without resort to such hard boundaries. In so doing, some new indices of polarization, segmentation and commonality of distribution are developed in the context of a decomposition of the Gini coefficient and the roles of, and relationships between, these groups in societal income inequality, poverty, polarization and societal segmentation are examined. What emerges for the Eurozone as an entity is a four-class, increasingly unequal polarizing structure with income growth in all four classes. With regard to individual constituent nation class membership, some advanced, some fell back, with most exhibiting significant polarizing behaviour. However, in the face of increasing overall Eurozone inequality, constituent nations were becoming increasingly similar in distribution, which can be construed as characteristic of a more cohesive society. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality)
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